1. Field of the Invention
The present invention generally relates to energy storage mediums, and more particularly to a real-time pricing controller and corresponding method for controlling an energy storage medium that is connected to an environmental control system.
2. Description of Related Art
Many electric utility companies are unable to generate enough electricity through conventional means to meet peak customer demand. Because of the enormous capital and environmental costs associated with building new power plants, these utilities offer incentives to their customers to reduce peak electrical consumption. These utility incentives encourage electrical consumers to shift energy consumption to those periods when reserve generating capacity is available. The incentives are typically provided in the form of an energy rate structure, and the real-time pricing (RTP) structure is rapidly gaining popularity.
RTP is a time-varying energy rate that takes into account the time-dependent variation in the cost of producing electricity. With a RTP structure, utility companies can adjust energy rates based on actual time-varying marginal costs, thereby providing an accurate and timely stimulus for encouraging customers to lower demand when marginal costs are high. An example of an RTP rate structure for a 24 hour period is shown in FIG. 1.
RTP differs from traditional time-of-day (TOD) or time-of-use (TOU) power rates in two primary ways. First, the demand charge of a TOD or TOU energy pricing structure is either eliminated or greatly reduced. Secondly, the rates in an RTP scheme may be altered more frequently (e.g., every hour) and with much less prior notice (i.e., one day or less).
When RTP is in use, a utility cost schedule for a given time interval is periodically provided to utility customers. Generally, the price schedule is provided the day before (day-ahead) or hour before (hour-ahead) the rate will take effect. In day-ahead pricing, utility customers are given price levels for the next day, and in hour-ahead pricing the customer receives the energy prices for the next hour.
In order for a utility customer to benefit from RTP, short-term adjustments must be made to curtail energy demands in response to periods with higher energy prices. One method of accomplishing this objective is by supplementing environmental conditioning systems with energy storage mediums. With these energy storage devices, external power consumption is decreased by drawing upon the energy reserves of the energy storage medium during periods having higher energy rates and generating energy reserves during intervals with lower energy prices.
To obtain the maximum benefit from RTP, a system must have access to energy demand and consumption information. Furthermore, the ability to project future load requirements is generally necessary. However, because energy prices of a RTP pricing structure change frequently and energy usage continually varies, an RTP cost function must be constantly minimized in order for a utility customer to receive the available cost savings. The discrete RTP cost function is given by ##EQU1##
where:
K is an interval of the RTP schedule; PA1 P.sub.k is the average electrical power (kW) consumed during interval k; PA1 J.sub.RTP is the cost to the customer; PA1 Re.sub.k is the energy cost during interval k (which is typically adjusted 24 times daily as shown in FIG. 1); PA1 .DELTA.t is an interval duration; and PA1 S is the number of intervals in the optimization time horizon.
Note that in subsequent discussions, the stage K will be replaced with an H to denote the hour of the day (in military time) since a typical interval length is one hour. However, it should be noted that the control strategy is appropriate for any interval length as long as the demand charge has been eliminated or reduced.
The state and control variable trajectories that minimize the RTP cost function can be found analytically (See L. S. Pontryagin, V. G. Boltyanskii, R. V. Gamkrelidze, and E. F. Mishchenko, "The Mathematical Theory of Optimal Process," Wiley-Interscience (1962)), numerically (See R. Bellman, "Dynamic Programming," Princeton University Press (1957)), or with genetic algorithms (See D. E. Goldberg, "Genetic Algorithms in Search, Optimization & Machine Learning," Addison-Wesley Publishing Company, Inc. (1989)), respectively. Unfortunately, each of these approaches requires significant expertise to formulate solutions and mathematically implement. In addition, a significant amount of computer resources (including memory) are required to generate a solution. Therefore, it is typically impractical to solve the optimal control problem of an energy storage medium in real time using realistic non-linear component models.
In view of the foregoing, it is one object of the present invention to provide a controller of an energy storage medium that minimizes the integrated cost function of a real-time-pricing utility service yet is simple to implement, computationally efficient, requires minimal memory, and is robust. Furthermore, additional advantages and features of the present invention will become apparent from the subsequent description and claims taken in conjunction with the accompanying drawings.